Rossmann_Sales_Prediction_Part_2_Vithika_Karan_ipynb.ipynb

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        "<a href=\"https://colab.research.google.com/github/vithika-karan/Retail-Sales-Prediction/blob/main/Rossmann_Sales_Prediction_Part_2_Vithika_Karan_ipynb.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "###Random Forest Hyperparameter Tuning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "Etk2kXGaTRSg"
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      "source": [
        "#Importing important libraries and modules\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "plt.rcParams.update({'figure.figsize':(8,5),'figure.dpi':100})\n",
        "from datetime import datetime\n",
        "\n",
        "import warnings    \n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "YFzhdh5jRlnz"
      },
      "outputs": [],
      "source": [
        "#metrics import\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.metrics import r2_score\n",
        "#importing model \n",
        "from sklearn.ensemble import RandomForestRegressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "dRAxp_qDb2xc"
      },
      "outputs": [],
      "source": [
        "#reading data\n",
        "df2 = pd.read_csv(\"/content/drive/MyDrive/ALMABETTER/CAPSTONE PROJECTS/Retail Sales Prediction - Vithika Karan/Cleaned Data/Cleaned_data.csv\",parse_dates=['Date'])\n",
        "comparison_df = pd.read_csv(\"/content/drive/MyDrive/ALMABETTER/CAPSTONE PROJECTS/Retail Sales Prediction - Vithika Karan/Cleaned Data/results.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "yS53Y39hdKwL"
      },
      "outputs": [],
      "source": [
        "#setting date and store as index\n",
        "df2.set_index(['Date','Store'],inplace=True)\n",
        "#sorting index following the time series\n",
        "df2.sort_index(inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "comparison_df.drop('Unnamed: 0',axis=1,inplace=True)\n",
        "comparison_df"
      ],
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              "      <th>Train_MAE</th>\n",
              "      <th>Train_MSE</th>\n",
              "      <th>Train_RMSE</th>\n",
              "      <th>Train_R2</th>\n",
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              "      <th>Test_RMSE</th>\n",
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              "      <th>0</th>\n",
              "      <td>Decision Tree Regressor</td>\n",
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              "                Model_Name  Train_MAE  ...   Test_R2  Test_Adj_R2\n",
              "0  Decision Tree Regressor    0.00000  ...  0.915750     0.915700\n",
              "1  Random Forest Regressor    0.00304  ...  0.955673     0.955647\n",
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>Customers</th>\n",
              "      <th>Promo</th>\n",
              "      <th>StateHoliday</th>\n",
              "      <th>SchoolHoliday</th>\n",
              "      <th>CompetitionDistance</th>\n",
              "      <th>Promo2</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>WeekOfYear</th>\n",
              "      <th>DayOfYear</th>\n",
              "      <th>CompetitionOpen</th>\n",
              "      <th>Promo2Open</th>\n",
              "      <th>Promo2running</th>\n",
              "      <th>DayOfWeek_1</th>\n",
              "      <th>DayOfWeek_2</th>\n",
              "      <th>DayOfWeek_3</th>\n",
              "      <th>DayOfWeek_4</th>\n",
              "      <th>DayOfWeek_5</th>\n",
              "      <th>DayOfWeek_6</th>\n",
              "      <th>DayOfWeek_7</th>\n",
              "      <th>StoreType_a</th>\n",
              "      <th>StoreType_b</th>\n",
              "      <th>StoreType_c</th>\n",
              "      <th>StoreType_d</th>\n",
              "      <th>Assortment_a</th>\n",
              "      <th>Assortment_b</th>\n",
              "      <th>Assortment_c</th>\n",
              "      <th>Sales</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th>Store</th>\n",
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              "      <th>2013-01-01</th>\n",
              "      <th>85</th>\n",
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            "text/plain": [
              "                  Customers  Promo  ...  Assortment_c     Sales\n",
              "Date       Store                    ...                        \n",
              "2013-01-01 85      0.083785      0  ...           0.0  0.101562\n",
              "\n",
              "[1 rows x 28 columns]"
            ]
          },
          "metadata": {},
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        }
      ],
      "source": [
        "df2.head(1)"
      ]
    },
    {
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      "execution_count": 7,
      "metadata": {
        "id": "gAIsds6fYDgx"
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      "source": [
        "#slicing the most recent six weeks and creating train and test set\n",
        "#train\n",
        "start_train = pd.to_datetime(\"2013-01-01\")\n",
        "end_train = pd.to_datetime(\"2015-06-14\")\n",
        "df_train = df2.loc[start_train:end_train]\n",
        "#test\n",
        "start_test = pd.to_datetime(\"2015-06-15\")\n",
        "end_test = pd.to_datetime(\"2015-07-31\")\n",
        "df_test = df2.loc[start_test:end_test]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
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        },
        "id": "JWnFL4wvYHtO",
        "outputId": "c1289774-85d1-477d-85dc-ee2ea6bb2a00"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The shape of X_train is: (798498, 27)\n",
            "The shape of y_train is: (798498, 1)\n",
            "The shape of X_test is: (45852, 27)\n",
            "The shape of y_test is: (45852, 1)\n"
          ]
        }
      ],
      "source": [
        "#X and y split for train and test\n",
        "X_train = df_train.drop('Sales',axis=1)\n",
        "y_train = df_train[['Sales']]\n",
        "X_test = df_test.drop('Sales',axis=1)\n",
        "y_test = df_test[['Sales']]\n",
        "print(f'The shape of X_train is: {X_train.shape}')\n",
        "print(f'The shape of y_train is: {y_train.shape}')\n",
        "print(f'The shape of X_test is: {X_test.shape}')\n",
        "print(f'The shape of y_test is: {y_test.shape}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "HyjoMKlkSDuA"
      },
      "outputs": [],
      "source": [
        "# Create model\n",
        "rf_tuned = RandomForestRegressor()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#import\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "#grid\n",
        "random_grid = {'bootstrap': [True, False],\n",
        " 'max_depth': [ 90, 100, None],\n",
        " 'max_features': ['auto', 'sqrt'],\n",
        " 'min_samples_leaf': [1, 2, 4],\n",
        " 'min_samples_split': [2, 5, 8],\n",
        " 'n_estimators': [100, 130, 150]}\n",
        "\n",
        "rf_random = RandomizedSearchCV(estimator = rf_tuned, param_distributions = random_grid, n_iter = 10, cv = 3, verbose=2, random_state=42)\n",
        "\n",
        "#fitting\n",
        "rf_random.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sHgkVt_i2cYk",
        "outputId": "e665bfb8-aa27-4ea9-a065-6c7798cddda3"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
            "[CV] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=8, n_estimators=100; total time= 5.7min\n",
            "[CV] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=8, n_estimators=100; total time= 5.8min\n",
            "[CV] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=8, n_estimators=100; total time= 5.8min\n",
            "[CV] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 7.2min\n",
            "[CV] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 7.3min\n",
            "[CV] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time= 7.2min\n",
            "[CV] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 2.9min\n",
            "[CV] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 2.9min\n",
            "[CV] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=150; total time= 3.0min\n",
            "[CV] END bootstrap=True, max_depth=90, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 6.6min\n",
            "[CV] END bootstrap=True, max_depth=90, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 6.5min\n",
            "[CV] END bootstrap=True, max_depth=90, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 6.7min\n",
            "[CV] END bootstrap=False, max_depth=90, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 9.3min\n",
            "[CV] END bootstrap=False, max_depth=90, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 9.3min\n",
            "[CV] END bootstrap=False, max_depth=90, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 9.1min\n",
            "[CV] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 6.5min\n",
            "[CV] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 6.4min\n",
            "[CV] END bootstrap=True, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time= 6.2min\n",
            "[CV] END bootstrap=False, max_depth=100, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=10.8min\n",
            "[CV] END bootstrap=False, max_depth=100, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=10.7min\n",
            "[CV] END bootstrap=False, max_depth=100, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=11.1min\n",
            "[CV] END bootstrap=False, max_depth=90, max_features=sqrt, min_samples_leaf=1, min_samples_split=8, n_estimators=130; total time= 3.1min\n",
            "[CV] END bootstrap=False, max_depth=90, max_features=sqrt, min_samples_leaf=1, min_samples_split=8, n_estimators=130; total time= 3.1min\n",
            "[CV] END bootstrap=False, max_depth=90, max_features=sqrt, min_samples_leaf=1, min_samples_split=8, n_estimators=130; total time= 3.1min\n",
            "[CV] END bootstrap=False, max_depth=100, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 3.2min\n",
            "[CV] END bootstrap=False, max_depth=100, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 3.7min\n",
            "[CV] END bootstrap=False, max_depth=100, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=150; total time= 3.6min\n",
            "[CV] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 2.2min\n",
            "[CV] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 1.8min\n",
            "[CV] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time= 2.1min\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=3, estimator=RandomForestRegressor(),\n",
              "                   param_distributions={'bootstrap': [True, False],\n",
              "                                        'max_depth': [90, 100, None],\n",
              "                                        'max_features': ['auto', 'sqrt'],\n",
              "                                        'min_samples_leaf': [1, 2, 4],\n",
              "                                        'min_samples_split': [2, 5, 8],\n",
              "                                        'n_estimators': [100, 130, 150]},\n",
              "                   random_state=42, verbose=2)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#best para\n",
        "rf_random.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EJuy43nzzJj2",
        "outputId": "84b6d73b-14f1-44a7-f644-84530f608663"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'bootstrap': True,\n",
              " 'max_depth': None,\n",
              " 'max_features': 'auto',\n",
              " 'min_samples_leaf': 1,\n",
              " 'min_samples_split': 2,\n",
              " 'n_estimators': 100}"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "RKayt6B1SRaY"
      },
      "outputs": [],
      "source": [
        "#save the best parameters\n",
        "random_t = rf_random.best_estimator_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "uwXgCgxJWcs1"
      },
      "outputs": [],
      "source": [
        "#Columns needed to compare metrics\n",
        "comparison_columns = ['Model_Name', 'Train_MAE', 'Train_MSE', 'Train_RMSE', 'Train_R2', 'Train_Adj_R2' ,'Test_MAE', 'Test_MSE', 'Test_RMSE', 'Test_R2', 'Test_Adj_R2']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "8L888VNWSnNx"
      },
      "outputs": [],
      "source": [
        "#function to evaluate the model\n",
        "def model_evaluation(model_name,model_variable,X_train,y_train,X_test,y_test):\n",
        "  ''' This function predicts and evaluates various models for regression algorithms, visualizes results \n",
        "      and creates a dataframe that compares the various models.'''\n",
        "      \n",
        "  #making predictions\n",
        "  y_pred_train = model_variable.predict(X_train)\n",
        "  y_pred_test = model_variable.predict(X_test)\n",
        "\n",
        "  # Plot the test results\n",
        "  a = y_test.copy()\n",
        "  a['Pred Sales'] = y_pred_test.tolist()\n",
        "  df_plot = a.reset_index(level=['Date'])\n",
        "  plot = df_plot.groupby('Date')['Sales','Pred Sales'].sum()\n",
        "  sns.lineplot(data = plot)\n",
        "  plt.ylabel(\"Total Sales and Predicted Sales\")\n",
        "  plt.xticks(rotation = 25)\n",
        "\n",
        "  #calculate metrics and print the results for test set\n",
        "  #Mean Absolute Error or MAE\n",
        "  MAE_train = round(mean_absolute_error(y_train,y_pred_train),6)\n",
        "  MAE_test = round(mean_absolute_error(y_test,y_pred_test),6)\n",
        "  #Mean Squared Error or MSE\n",
        "  MSE_train = round(mean_squared_error(y_train,y_pred_train),6)\n",
        "  MSE_test = round(mean_squared_error(y_test,y_pred_test),6)\n",
        "  #Root Mean Squared Error or RMSE\n",
        "  RMSE_train = round(mean_squared_error(y_train,y_pred_train,squared=False),6)\n",
        "  RMSE_test = round(mean_squared_error(y_test,y_pred_test,squared=False),6)\n",
        "  #R2\n",
        "  R2_train = round(r2_score(y_train, y_pred_train),6)\n",
        "  R2_test = round(r2_score(y_test, y_pred_test),6)\n",
        "  #Adjusted R2\n",
        "  Adj_r2_train = round(1 - (1-r2_score(y_train, y_pred_train)) * (len(y_train)-1)/(len(y_train)-X_train.shape[1]-1),6)\n",
        "  Adj_r2_test = round(1 - (1-r2_score(y_test, y_pred_test)) * (len(y_test)-1)/(len(y_test)-X_test.shape[1]-1),6)\n",
        "\n",
        "  #printing test results\n",
        "  print(f'The Mean Absolute Error for the validation set is {MAE_test}')\n",
        "  print(f'The Mean Squared Error for the validation set is {MSE_test}')\n",
        "  print(f'The Root Mean Squared Error for the validation set is {RMSE_test}')\n",
        "  print(f'The R^2 for the validation set is {R2_test}')\n",
        "  print(f'The Adjusted R^2 for the validation set is {Adj_r2_test}')\n",
        "\n",
        "  #Saving our results\n",
        "  global comparison_columns\n",
        "  metric_scores = [model_name,MAE_train,MSE_train,RMSE_train,R2_train,Adj_r2_train,MAE_test,MSE_test,RMSE_test,R2_test,Adj_r2_test]\n",
        "  final_dict = dict(zip(comparison_columns,metric_scores))\n",
        "  return [final_dict]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "11uu2aBdSVLY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        },
        "outputId": "147910e7-aa93-4169-b63c-50807a1e4d6d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Mean Absolute Error for the validation set is 0.010342\n",
            "The Mean Squared Error for the validation set is 0.000244\n",
            "The Root Mean Squared Error for the validation set is 0.015617\n",
            "The R^2 for the validation set is 0.955878\n",
            "The Adjusted R^2 for the validation set is 0.955852\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#evaluate tuned model\n",
        "random_tuned = model_evaluation('Random Forest Tuned',random_t,X_train,y_train,X_test,y_test)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#add to comparison_df\n",
        "comparison_df = comparison_df.append(random_tuned)"
      ],
      "metadata": {
        "id": "bmWObWPh2Vue"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#comparison_df\n",
        "comparison_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "n0SrshP_0c8t",
        "outputId": "7641d8b4-53a2-44ff-8be0-eade2076e429"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model_Name</th>\n",
              "      <th>Train_MAE</th>\n",
              "      <th>Train_MSE</th>\n",
              "      <th>Train_RMSE</th>\n",
              "      <th>Train_R2</th>\n",
              "      <th>Train_Adj_R2</th>\n",
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              "      <th>Test_MSE</th>\n",
              "      <th>Test_RMSE</th>\n",
              "      <th>Test_R2</th>\n",
              "      <th>Test_Adj_R2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Decision Tree Regressor</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000047</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.014203</td>\n",
              "      <td>0.000466</td>\n",
              "      <td>0.021580</td>\n",
              "      <td>0.915750</td>\n",
              "      <td>0.915700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Random Forest Regressor</td>\n",
              "      <td>0.00304</td>\n",
              "      <td>0.000022</td>\n",
              "      <td>0.004640</td>\n",
              "      <td>0.996143</td>\n",
              "      <td>0.996143</td>\n",
              "      <td>0.010328</td>\n",
              "      <td>0.000245</td>\n",
              "      <td>0.015653</td>\n",
              "      <td>0.955673</td>\n",
              "      <td>0.955647</td>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Random Forest Tuned</td>\n",
              "      <td>0.00304</td>\n",
              "      <td>0.000021</td>\n",
              "      <td>0.004622</td>\n",
              "      <td>0.996173</td>\n",
              "      <td>0.996173</td>\n",
              "      <td>0.010342</td>\n",
              "      <td>0.000244</td>\n",
              "      <td>0.015617</td>\n",
              "      <td>0.955878</td>\n",
              "      <td>0.955852</td>\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-641bc191-6c86-4128-a9ee-e041302cf58d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-641bc191-6c86-4128-a9ee-e041302cf58d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                Model_Name  Train_MAE  ...   Test_R2  Test_Adj_R2\n",
              "0  Decision Tree Regressor    0.00000  ...  0.915750     0.915700\n",
              "1  Random Forest Regressor    0.00304  ...  0.955673     0.955647\n",
              "0      Random Forest Tuned    0.00304  ...  0.955878     0.955852\n",
              "\n",
              "[3 rows x 11 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gp4G9RgAofrF"
      },
      "source": [
        "####Feature Importance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "vZW5MmJvpZPO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        },
        "outputId": "5627a919-c73c-4afb-ce4d-02a0ae23ebeb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6cfbeed1d0>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#visualising feature importance of XGB\n",
        "feature_imp = pd.DataFrame({\"Variable\": X_test.columns,\"Importance\": random_t.feature_importances_})\n",
        "feature_imp.sort_values(by=\"Importance\", ascending=False, inplace = True)\n",
        "sns.barplot(x=feature_imp['Importance'], y= feature_imp['Variable'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kM_kRskWqQNR"
      },
      "source": [
        "###Model Performance and Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "cPguNCTtqV3B",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "76fce144-4ad8-4765-b95b-f8e07e2da82c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-f2ca0dbb-36a1-49d9-a8a6-44f3f0a8384f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model_Name</th>\n",
              "      <th>Train_MAE</th>\n",
              "      <th>Train_MSE</th>\n",
              "      <th>Train_RMSE</th>\n",
              "      <th>Train_R2</th>\n",
              "      <th>Train_Adj_R2</th>\n",
              "      <th>Test_MAE</th>\n",
              "      <th>Test_MSE</th>\n",
              "      <th>Test_RMSE</th>\n",
              "      <th>Test_R2</th>\n",
              "      <th>Test_Adj_R2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Decision Tree Regressor</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000047</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.014203</td>\n",
              "      <td>0.000466</td>\n",
              "      <td>0.021580</td>\n",
              "      <td>0.915750</td>\n",
              "      <td>0.915700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Random Forest Regressor</td>\n",
              "      <td>0.00304</td>\n",
              "      <td>0.000022</td>\n",
              "      <td>0.004640</td>\n",
              "      <td>0.996143</td>\n",
              "      <td>0.996143</td>\n",
              "      <td>0.010328</td>\n",
              "      <td>0.000245</td>\n",
              "      <td>0.015653</td>\n",
              "      <td>0.955673</td>\n",
              "      <td>0.955647</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Random Forest Tuned</td>\n",
              "      <td>0.00304</td>\n",
              "      <td>0.000021</td>\n",
              "      <td>0.004622</td>\n",
              "      <td>0.996173</td>\n",
              "      <td>0.996173</td>\n",
              "      <td>0.010342</td>\n",
              "      <td>0.000244</td>\n",
              "      <td>0.015617</td>\n",
              "      <td>0.955878</td>\n",
              "      <td>0.955852</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f2ca0dbb-36a1-49d9-a8a6-44f3f0a8384f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f2ca0dbb-36a1-49d9-a8a6-44f3f0a8384f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f2ca0dbb-36a1-49d9-a8a6-44f3f0a8384f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                Model_Name  Train_MAE  ...   Test_R2  Test_Adj_R2\n",
              "0  Decision Tree Regressor    0.00000  ...  0.915750     0.915700\n",
              "1  Random Forest Regressor    0.00304  ...  0.955673     0.955647\n",
              "0      Random Forest Tuned    0.00304  ...  0.955878     0.955852\n",
              "\n",
              "[3 rows x 11 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "#Taking a look of our final comparison dataframe\n",
        "comparison_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "qFxe8vxLqbku",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 474
        },
        "outputId": "2358bb0e-4f22-4fce-c683-97eb682a29d9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6cfbc91dd0>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#visualising our comparison df for Test R2\n",
        "sns.barplot(y=comparison_df['Model_Name'], x = comparison_df['Test_R2'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Psfm5VAnrw07"
      },
      "source": [
        "####Random Forest vs Baseline Model"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#test values\n",
        "baseline_r2 = 0.915750\n",
        "random_r2 = 0.955673\n",
        "r_tuned_r2 = 0.955878"
      ],
      "metadata": {
        "id": "YjgNnZ3x5PFw"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "bdpJQf9ir4W3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f170ef04-2839-43a4-e51a-c92ef25ae10f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Performance\n",
            "Improvement of 4.36 % was seen in Random Forest against Decision Tree.\n"
          ]
        }
      ],
      "source": [
        "#improvement % \n",
        "improvement_r = ((random_r2 - baseline_r2)/baseline_r2)*100\n",
        "print('Model Performance')\n",
        "print(f'Improvement of {round(improvement_r,3)} % was seen in Random Forest against Decision Tree.')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mr8jaTHMv9KS"
      },
      "source": [
        "####Random Forest Tuned vs Baseline and Random Forest Models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "jFWgzj_8voU-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6102a5a5-dbba-4c65-83cc-93d0166dd116"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Performance\n",
            "Improvement of 4.382 % was seen in Random Forest Tuned against Decision Tree.\n"
          ]
        }
      ],
      "source": [
        "#improvement % of tuned vs baseline\n",
        "\n",
        "improvement_r = ((r_tuned_r2 - baseline_r2)/baseline_r2)*100\n",
        "print('Model Performance')\n",
        "print(f'Improvement of {round(improvement_r,3)} % was seen in Random Forest Tuned against Decision Tree.')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "m4BTan9SwYEn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6b2591f7-74a3-4db5-cc6f-3257b8b52ea6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model Performance\n",
            "Improvement of 0.021 % was seen in Random Forest Tuned against Simple Random Forest.\n"
          ]
        }
      ],
      "source": [
        "#improvement % of tuned vs simple random forest\n",
        "improvement_r = ((r_tuned_r2 - random_r2)/random_r2)*100\n",
        "print('Model Performance')\n",
        "print(f'Improvement of {round(improvement_r,3)} % was seen in Random Forest Tuned against Simple Random Forest.')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZZEjD929wuVA"
      },
      "source": [
        "Observation\n",
        "\n",
        "* The maximum R^2 was seen in tuned Random Forest model with the value 0.955878 which was only 0.021% improved from a simple random forest model. \n",
        "* It seems like we have maxed out on performance with the data and these models."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w6h6n-PcxSzA"
      },
      "source": [
        "###Store wise Sales Predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "metadata": {
        "id": "D5zFmr2g1E1p"
      },
      "outputs": [],
      "source": [
        "#predictions \n",
        "y_pred_test = random_t.predict(X_test)\n",
        "six_weeks_sales_df = y_test.copy()\n",
        "six_weeks_sales_df['Pred_Sales'] = y_pred_test.tolist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "metadata": {
        "id": "0OFFF6oV1rjM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "354c66fd-755b-40d7-846e-df220ab0e583"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-e31d5fd2-5615-416b-a3b6-514f35b60d21\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>Sales</th>\n",
              "      <th>Pred_Sales</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th>Store</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">2015-06-15</th>\n",
              "      <th>1</th>\n",
              "      <td>0.132801</td>\n",
              "      <td>0.131027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.195086</td>\n",
              "      <td>0.194641</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.260355</td>\n",
              "      <td>0.267028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.298380</td>\n",
              "      <td>0.281238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.187914</td>\n",
              "      <td>0.181849</td>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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            "text/plain": [
              "                     Sales  Pred_Sales\n",
              "Date       Store                      \n",
              "2015-06-15 1      0.132801    0.131027\n",
              "           2      0.195086    0.194641\n",
              "           3      0.260355    0.267028\n",
              "           4      0.298380    0.281238\n",
              "           5      0.187914    0.181849"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ],
      "source": [
        "#head\n",
        "six_weeks_sales_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#importing minmax scaler\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "#fitting to the original data\n",
        "cleaned_unscaled_data= pd.read_csv(\"/content/drive/MyDrive/ALMABETTER/CAPSTONE PROJECTS/Retail Sales Prediction - Vithika Karan/Cleaned Data/Cleaned_Unscaled_data.csv\")\n",
        "#fitting numerical columns\n",
        "numerical_cols = ['Sales']\n",
        "scaler = MinMaxScaler()\n",
        "scaler.fit(cleaned_unscaled_data[numerical_cols])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EcyCu1WSBCUj",
        "outputId": "57bace04-9bb5-46f2-ab3c-b8916ae4e68f"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MinMaxScaler()"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {
        "id": "oKwtvUjc1wuD"
      },
      "outputs": [],
      "source": [
        "#inverse\n",
        "six_weeks_sales_df[['Sales']] = scaler.inverse_transform(six_weeks_sales_df[['Sales']])\n",
        "six_weeks_sales_df[['Pred_Sales']] = scaler.inverse_transform(six_weeks_sales_df[['Pred_Sales']])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#sales vs predictions\n",
        "six_weeks_sales_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "vEe3w0H4NSG0",
        "outputId": "9114f045-666b-4877-8808-7ba7c296dd50"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>Sales</th>\n",
              "      <th>Pred_Sales</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th>Store</th>\n",
              "      <th></th>\n",
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              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">2015-06-15</th>\n",
              "      <th>1</th>\n",
              "      <td>5518.0</td>\n",
              "      <td>5444.30</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8106.0</td>\n",
              "      <td>8087.52</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10818.0</td>\n",
              "      <td>11095.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12398.0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>7808.0</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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            "text/plain": [
              "                    Sales  Pred_Sales\n",
              "Date       Store                     \n",
              "2015-06-15 1       5518.0     5444.30\n",
              "           2       8106.0     8087.52\n",
              "           3      10818.0    11095.28\n",
              "           4      12398.0    11685.72\n",
              "           5       7808.0     7555.99"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#locating\n",
        "six_weeks_sales_df.loc[('2015-06-15',5)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EFVVY7jvOk-h",
        "outputId": "238afa58-8945-43b6-f0ef-a66e5d2c441f"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sales         7808.00\n",
              "Pred_Sales    7555.99\n",
              "Name: (2015-06-15 00:00:00, 5), dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#locating\n",
        "six_weeks_sales_df.loc[('2015-07-28',56)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nUYsbETfPIYt",
        "outputId": "6b1ed854-6b7c-45ab-b083-c5d629805130"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sales         8400.00\n",
              "Pred_Sales    7616.54\n",
              "Name: (2015-07-28 00:00:00, 56), dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j3z_mpZK3IQ8"
      },
      "source": [
        "###Conclusion and Recommendations\n",
        "**Conclusion**\n",
        "\n",
        "Businesses use sales forecasts to determine what revenue they will be generating in a particular timespan to empower themselves with powerful and strategic business plans. Important decisions such as budgets, hiring, incentives, goals, acquisitions and various other growth plans are affected by the revenue the company is going to make in the coming months and for these plans to be as effective as they are planned to be it is important for these forecasts to also be as good.\n",
        "\n",
        "The work here forecasts the sales of the various Rossmann stores across Europe for the recent six weeks and compares the results from the models developed with the actual sales values.\n",
        "\n",
        "Some important conclusions drawn from the analysis are as follows:\n",
        "* there were more sales on Monday, probably because shops generally remain closed on Sundays which had the lowest sales in a week. This validates the hypothesis about this feature.\n",
        "* The positive effect of promotion on Customers and Sales is observable.\n",
        "* Most stores have competition distance within the range of 0 to 10 kms and had more sales than stores far away probably indicating competition in busy locations vs remote locations.\n",
        "* Store type B though being few in number had the highest sales average. The reasons include all three kinds of assortments specially assortment level b which is only available at type b stores and being open on sundays as well.\n",
        "* The outliers in the dataset showed justifiable behaviour. The outliers were either of store type b or had promotion going on which increased sales.\n",
        "* Random Forest Tuned Model gave the best results and only 0.021% improvement was seen from the basic random forest model which indicates that all the trends and patterns that could be captured by these models without overfitting were done and maximum level of performance achievable by the model was achieved.\n",
        "\n",
        "**Recommendations**\n",
        "* More stores should be encouraged for promotion. \n",
        "* Store type B should be increased in number.\n",
        "* There's a seasonality involved, hence the stores should be encouraged to promote and take advantage of the holidays.\n"
      ]
    }
  ],
  "metadata": {
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